Search Results for author: Zhenglong Sun

Found 6 papers, 4 papers with code

Prompt-based Learning for Unpaired Image Captioning

no code implementations26 May 2022 Peipei Zhu, Xiao Wang, Lin Zhu, Zhenglong Sun, Weishi Zheng, YaoWei Wang, Changwen Chen

Inspired by the success of Vision-Language Pre-Trained Models (VL-PTMs) in this research, we attempt to infer the cross-domain cue information about a given image from the large VL-PTMs for the UIC task.

Image Captioning Question Answering +2

Unpaired Image Captioning by Image-level Weakly-Supervised Visual Concept Recognition

no code implementations7 Mar 2022 Peipei Zhu, Xiao Wang, Yong Luo, Zhenglong Sun, Wei-Shi Zheng, YaoWei Wang, Changwen Chen

The image-level labels are utilized to train a weakly-supervised object recognition model to extract object information (e. g., instance) in an image, and the extracted instances are adopted to infer the relationships among different objects based on an enhanced graph neural network (GNN).

Image Captioning Object Recognition

Unsupervised Monocular Depth Perception: Focusing on Moving Objects

1 code implementation30 Aug 2021 Hualie Jiang, Laiyan Ding, Zhenglong Sun, Rui Huang

We first propose an outlier masking technique that considers the occluded or dynamic pixels as statistical outliers in the photometric error map.

Autonomous Driving Motion Estimation

BORM: Bayesian Object Relation Model for Indoor Scene Recognition

1 code implementation1 Aug 2021 Liguang Zhou, Jun Cen, Xingchao Wang, Zhenglong Sun, Tin Lun Lam, Yangsheng Xu

First, we utilize an improved object model (IOM) as a baseline that enriches the object knowledge by introducing a scene parsing algorithm pretrained on the ADE20K dataset with rich object categories related to the indoor scene.

Scene Recognition

DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular Videos

1 code implementation3 Mar 2020 Hualie Jiang, Laiyan Ding, Zhenglong Sun, Rui Huang

Unsupervised learning of depth and ego-motion from unlabelled monocular videos has recently drawn great attention, which avoids the use of expensive ground truth in the supervised one.

Autonomous Driving Monocular Depth Estimation +1

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